This guide details how to set up a Disaggregated Prefill-Decode (P/D) architecture across two nodes equipped with NVIDIA RTX PRO 6000 (Blackwell SM120) GPUs.

By separating the compute-heavy prefill phase from the memory-bandwidth-heavy decode phase, you prevent cross-interference and significantly improve both Time To First Token (TTFT) and overall cluster goodput.

• GPUs: 2x Nodes, each with RTX PRO 6000 GPUs (96GB GDDR7).

jq — lightweight, flexible command-line JSON processor

• Basics
• Navigation
• Arrays
• Objects
• Filtering & Selection

All packages, except for Tini have been added to termux-root. To install them, simply pkg install root-repo && pkg install docker. This will install the whole docker suite, left only Tini to be compiled manually.

Table of Contents

• General Usage
• Shortcuts
• Commands
• Scripting tmux
• Configuring tmux

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.